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Stock market prediction (SMP) is challenging due to its uncertainty, nonlinearity, and volatility. Machine learning models such as recurrent neural networks (RNNs) have been widely used in SMP and have achieved high performance in terms of "minimum error". However, in the context of SMP, using "least cost" to measure performance makes more sense. False Positive Errors (FPE) can lead to significant trading losses, while False Negative Errors (FNE) can result in missed opportunities. Minimizing FPE is critical for investors. In practice, some errors may result in irreparable losses, so measuring costs based on data is important. In this research, we propose a new method called generalized loss CNN-BiLSTM (GL-CNN-BiLSTM), where the cost of each datum can be dynamically calculated based on the difficulty of the data. We verify the effectiveness of GL-CNN-BiLSTM on Shanghai, Hong Kong, and NASDAQ stock exchange data. Experimental results show that although there is no significant difference in the accuracy and winning rate between GL-CNN-BiLSTM and other methods, GL-CNN-BiLSTM achieves the highest rate of return on the test data. [ABSTRACT FROM AUTHOR]

The effects of dams on carbon dioxide (CO2) fluxes in downstream lakes remain elusive. Here we combined eddy covariance observations and random forest models to examine multi‐decadal variations in CO2 fluxes in the Poyang Lake, the largest freshwater lake in China, and quantified the contribution of the Three Gorges Dam (TGD), the world's largest hydraulic project. We found the lake fluctuated between CO2 source and sink in 1961–2016, and tended to be CO2 sink in the post‐TGD period (2003–2016) when vegetation expanded early and spatially due to declining water level. TGD can explain approximately 6% of the total differences in annual CO2 fluxes, with major contributions in the impoundment period (up to 22% in middle September to October). The results show a positive side of operational major hydraulic projects on lake carbon sink, and probably caution the negative side of carbon release after dam removal. Plain Language Summary: In the past century, dams have significantly altered the hydrological connectivity between rivers and lakes, which affect CO2 exchange in the downstream lake systems. As the largest freshwater lake in China, Poyang Lake has also undergone drastic hydrological changes, attributable largely to the operation of the Three Gorges Dam (TGD), the world's largest hydraulic project ever, in 2003. Based on flux observations and machine learning method, we show that annual lake CO2 exchange shifted toward carbon sink during 1961–2016. The TGD has a major impact on lake CO2 fluxes, especially during the impoundment stage in middle September–October, explaining 22% of the flux differences between the pre‐ and post‐TGD period. The results show a positive side of hydraulic projects albeit their adverse impact on ecological protection. Key Points: Poyang Lake as a CO2 source or sink significantly depends on water levelPoyang Lake became a CO2 sink since the Three Gorges Dam operation in 2003Dam explains 22% of differences in CO2 fluxes in autumn impoundment period [ABSTRACT FROM AUTHOR]

Mangrove ecosystems can be both significant sources and sinks of greenhouse gases. The restoration of mangrove forests is increasingly used as a natural climate solution tool to mitigate climate change. However, the estimates of carbon exchanges remain unclear, especially from restored mangroves. In this study, we observed the temporal variations in carbon dioxide (CO2) and methane (CH4) fluxes and their biophysical controls for 4 years, based on a closed-path eddy covariance (EC) system. The measurements were conducted in a mangrove wetland park with 14-year-old restored mangroves surrounded by open waters in Guangdong Province, China. The EC measurements showed that the mangrove ecosystem acted as a CO2 source with a net CO2 ecosystem exchange (NEE) of 305 g C m−2 from January 2019 to May 2020 by the 5-m tower measurement. After the tower was adjusted to 10 m, the mangrove showed a CO2 sink with an NEE of −345 g C m−2 from June 2020 to December 2022. The change in tower height influenced the interpretation of interannual trends on NEE. There were no significant interannual trends in the gross primary productivity (GPP) and the ecosystem respiration (Re) values. The change from CO2 source to sink may be attributed to the decrease in land surface proportion by the tower replacement, which reduces the proportion of the mangrove canopy respiration and, therefore, captures lower CO2 fluxes from open waters. The restored mangroves indicated strong CH4 sources of 23.2–26.3 g C m−2 a−1. According to the random forest analysis, the land surface proportion, radiation, and relative humidity were the three most important predictors of NEE, while the CH4 flux was most sensitive to air temperature. Compared to the natural and long-term restored mangroves, this 14-year-old restored mangrove had not yet achieved a maximum carbon sequestration capability. Our study highlights the need for the careful design of long-term observations from restored mangroves and proposes future needs in the context of carbon neutrality. [ABSTRACT FROM AUTHOR]